In this paper,
we propose a novel method for separately estimating
spectral distributions from images captured by a typical RGB camera.
The proposed method allows us to separately estimate a spectral distribution
of illumination, reflectance, or camera sensitivity,
while recent hyperspectral cameras are limited
to capturing a joint spectral distribution from a scene.
In addition, the use of Bayesian inference makes it possible
to take into account prior information of
both spectral distributions and image noise as probability distributions.
As a result,
the proposed method can estimate spectral distributions in a unified way,
and it can enhance the robustness of the estimation against noise,
which conventional spectral-distribution estimation methods cannot.
The use of Bayesian inference also enables us
to obtain the confidence of estimation results.
In an experiment,
the proposed method is shown not only to outperform
conventional estimation methods in terms of RMSE
but also to be robust against noise.


Bayesian inference, camera sensitivity, illumination,
reflectance, spectral distribution


This work was supported by JSPS KAKENHI Grant Number JP18J20326.

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Introduction
Spectral distributions of illumination, the reflectance of objects,
and camera sensitivity play an important role in color image processing.
They decide the color of pixel values in images captured by digital cameras.
Spectral distribution is a function of wavelengths
and describes what wavelengths of light are included in illumination,
reflected by objects, or detected by imaging sensors.
Knowledge on spectral distributions is essential
for many computer vision tasks that use color information,
such as hyperspectral imaging [1]}, [2]},
color constancy [3]}, [4]}, [5]}, [6]}, [7]}, [8]},
color image enhancement [9]}, [10]}, [11]}, [12]},
and intrinsic image decomposition [13]}, [14]}, [15]}, [16]}.
